Revealing organic carbon sources fueling a coral reef food web in the Gulf of Mexico using stable isotopes and fatty acids
Bibliographic record
Abstract
Carbon and nitrogen stable isotopes and fatty acids (FAs) revealed primary producer organic carbon sources that fuel a coral reef food web with river influence. A stable isotope mixing model was used to assess the relative contribution of six different primary producers to nine of the most ubiquitous invertebrate and fish consumer's bulk carbon. Mangrove and phytoplankton were difficult to differentiate in some consumers; likely solutions involved one or the other but not both at the same time. FA concentration in upper trophic levels was corrected for the primary producer's relative contribution according to the mixing model, and FA retention was evaluated using a calculated trophic retention factor (TRF). The C18 FAs, 18:2ω6 and 18:3ω3, were plentiful in mangrove, sea grass, and green algae, but decreased across trophic levels with a TRF ≤ 1, probably due to decomposition of drifting leaves and then consumer metabolism. In contrast, macroalgae and phytoplankton FAs, 24:1ω9, and highly unsaturated fatty acids (HUFAs), arachidonic acid (ARA) 20:4ω6, docosapentanoic acid (DPA) 22:5ω3, and docosahexanoic acid (DHA) 22:6ω3, showed trophic accumulation (TRF > 1), while eicosapentanoic acid (EPA) 20:5ω3 had similar concentrations across trophic levels (TRF = 1), suggesting the following degrees of HUFA retention: DHA > ARA > EPA. This study indicates that phytoplankton are the major source of essential dietary nutrients for all fish, and that dietary energy from mangroves is transferred to juvenile fish Caranx hippos, while sea grass nonessential FAs are transferred to the entire food web. Moreover, among the species studied, the sea urchin Echinometra lucunter is the major consumer of brown and green algae, while red algae were also consumed by the surgeon fish Acanthurus chirurgus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".